Comparison of assembly-jam learning algorithms with fuzzy entropy measure for intelligent robot’s part micro-assembly
作者： Changman Son
DOI: 10.1007/S00170-019-04100-7
关键词:
摘要: Two assembly-jam learning algorithms are introduced for reducing the task performance time as well getting out of an in a robot’s part micro-assembly. The two then compared from viewpoint five factors. A comprehensive comparison results with other recent methods and discussions also described. split or unify to simplify regions situated near similar state on location map. This allows micro-assembly be continuously reiterated such that speed will faster. is achieved by fewest number robot joint control motions. generated technique minimize motions 1st algorithm, without wasting energy. Meanwhile, last formed minimized region unifying process 2nd algorithm. significantly reduce processes. degree uncertainty (measured fuzzy entropy function) associated used criterion determine most valid plan present input. show can successfully plans algorithms.
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